Phenotyping of children with abdominal pain.
<p><b>A.</b> Scatter plots based on three new variables generated by unsupervised Uniform Manifold Approximation and Projection (UMAP). Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) found 3 distinct phenotypes. <b>B.</b> Hierarchical...
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2025
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| Summary: | <p><b>A.</b> Scatter plots based on three new variables generated by unsupervised Uniform Manifold Approximation and Projection (UMAP). Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) found 3 distinct phenotypes. <b>B.</b> Hierarchical structure of identified phenotypes (Condensed Tree). The vertical axis (λ value = 1/ε, density scale) represents the density scale used by the HDBSCAN algorithm. HDBSCAN performs hierarchical clustering by varying the distance threshold that defines neighborhood connectivity (ε). Smaller ε (larger λ) corresponds to higher-density regions, while larger ε (smaller λ) represents broader, lower-density structures. Clusters that persist across a wider range of λ values (longer vertical branches) indicate more stable and well-defined groups. The horizontal axis shows the cluster hierarchy, corresponding to different cluster branches identified by HDBSCAN. Each branch represents a cluster that emerges and persists across density thresholds. The color of each branch indicates the number of data points (cluster size), as shown in the color bar on the right. <b>C.</b> Scatter plots color-coded by representative variables. Each plot is colored according to the representative variables that characterize the three phenotypes.</p> |
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